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Way to develop graphs and model machine learning

Árbore: e6d2356d12
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luis e6d2356d12 Selection, improvements on the state flow %!s(int64=6) %!d(string=hai) anos
browser e6d2356d12 Selection, improvements on the state flow %!s(int64=6) %!d(string=hai) anos
docker be46042d0d Implemented collaboration and server serialization %!s(int64=6) %!d(string=hai) anos
go 324f7c7ef5 Changed makefile %!s(int64=6) %!d(string=hai) anos
.drone.yml 51bba5042d Droning building Sun Jan 21 20:55:29 -02 2018 %!s(int64=6) %!d(string=hai) anos
.gitignore 9631547566 Fixing submodule issue %!s(int64=6) %!d(string=hai) anos
Makefile 80fd6e920b Droning building Sat Jan 20 18:55:28 -02 2018 %!s(int64=6) %!d(string=hai) anos
readme.md e0378dd50b Backdev %!s(int64=6) %!d(string=hai) anos

readme.md

TODO

Minimal idea

UI to create multipurpose tensorflow alike graphs, upload to the server pass inputs and do training fetch model and use it with new inputs distributed purpose to calculate several nodes across a network

implement common AI methods(funcs) reuse them

Frontend

  • Deal with svg relative mouse position
  • do an event from one socket to another highlighting the ones compatible for data type
  • Create something to add/remove links/nodes as in populating from the server
  • Colaborative editing through possibly websocket, by sharing states
  • const/var editor with multiple props
  • add types to sockets (litle label aside)

Backend

  • create http server that serializes and inspect run graphs (step by step)
  • Serve registry with information about inputs/outputs
  • Protocol to build and run a graph from frontend